Presentation: Tweet"Machine Learning == Automated TDD"
There is a lot of hype and mystique around Machine Learning these days. The combination of the words "machine" and "learning" induces hallucinations of intelligent machines that magically learn by soaking up Big Data and then both solving world hunger and making us rich while we lay on the beach sipping a cold one.
Worse yet, the esoteric and mathematical terminology of many Machine Learning textbooks and research papers fuels the mystique, resulting in the persona of the Data Scientist as the 21st century druid that mystically distills insight and knowledge from raw data.
However, just as normal programmers can write code without needing to understand Universal Turing Machines, power domains, or predicate transformers, we believe that normal programmers can use Machine Learning without needing to understand vectors, features, probability density, Jacobians, etc. In fact, the very essence of Machine Learning is creating code from a finite set of sample input/output pairs. This is something that programmers are already deeply familiar with; and in this talk, we will explain how Machine Learning is Test Driven Development performed by code (TDD).
